Enhance the IPL Prediction Model with Advanced Features

[FreeSpirit11]

Is your feature request related to a problem? Please describe.

The current IPL Prediction model in Project-Guidance/Machine Learning and Data Science/Intermediate/IPL Prediction/Regularisation - RIDGE_LASSO_HYBRID.ipynb lacks several advanced features that could significantly improve its performance and interpretability. Specifically, it does not include thorough feature selection, hyperparameter tuning, comprehensive feature engineering, outlier handling, enhanced model evaluation metrics, or ensemble methods.

Describe the solution you'd like.

I would like to enhance the existing model by implementing the following features:

1. Feature Selection: Analyze Lasso coefficients to identify and retain important features.
2. Hyperparameter Tuning: Experiment with different alpha values for Ridge, Lasso, and ElasticNet to optimize model performance.
3. Feature Engineering: Create new features based on domain knowledge to improve the model’s predictive power.
4. Outlier Handling: Detect and clean outliers from the dataset to ensure robust model training.
5. Model Evaluation: Evaluate models using additional metrics beyond RMSE, such as R-squared and Mean Absolute Error (MAE).
6. Ensemble Methods: Implement and evaluate ensemble techniques like Random Forest and Gradient Boosting for improved performance.

Describe alternatives you've considered.

As an alternative, I considered:

• Using only basic linear regression without regularization, but this often leads to overfitting and less robust predictions.
• Manually selecting features without Lasso, but this can be subjective and less effective.
• Avoiding hyperparameter tuning which would result in suboptimal model performance.
• Ignoring outliers which might skew the model’s performance and predictions.
• Using only RMSE for evaluation, but it doesn’t provide a complete picture of model accuracy.
• Relying on single models rather than ensembles, potentially leading to less accurate predictions.

Add any other context or screenshots about the feature request here.

Implementing these features will require modifications to the existing Regularisation - RIDGE_LASSO_HYBRID.ipynb file, including additional code for feature engineering, hyperparameter tuning with GridSearchCV, and evaluating model performance with ensemble methods. Visualizations such as feature importance plots from Random Forest and Gradient Boosting models will also be included.

Below is a brief outline of the changes to be made:

1. Feature Selection:

   • Use Lasso regression to identify important features.
   • Retain features with non-zero coefficients.

2. Hyperparameter Tuning:

   • Implement GridSearchCV for Ridge, Lasso, and ElasticNet to find optimal alpha values.

3. Feature Engineering:

   • Create new domain-specific features (e.g., RUNS_PER_MATCH).

4. Outlier Handling:

   • Detect outliers using Z-scores and remove them.

5. Model Evaluation:

   • Evaluate models using RMSE, R-squared, and MAE.

6. Ensemble Methods:

   • Implement and evaluate Random Forest and Gradient Boosting models.
   • Visualize feature importances from ensemble methods.

These enhancements aim to improve the overall robustness and accuracy of the IPL Prediction model.

[FreeSpirit11]

Hi, I have raised this issue . Please assign it to me.

[FreeSpirit11]

Hi @Kushal997-das , It is not a level 1 issue. Please assign it level 2.

[Kushal997-das]

@FreeSpirit11 Will see PR then will decide. Complete this project ASAP else will close.